Shiboleth Features

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Shiboleth is an AI-powered platform that automates consumer lending compliance by auditing customer interactions to detect and mitigate regulatory violations.
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Profile of Shiboleth
Overview
Analytics
What is Shiboleth
How to use Shiboleth & FAQs

Key Features of Shiboleth

Shiboleth is an AI-powered platform that automates consumer lending compliance for financial institutions. It uses advanced natural language processing and machine learning to automatically flag compliance violations in customer interactions, identify trends, raise mitigation actions, and process large volumes of data quickly. The system focuses on regulations like UDAAP and FDCPA, helping companies reduce manual audit work and costs while improving compliance coverage.
Automated Violation Detection: Automatically flags compliance violations in customer interactions using AI and NLP.
Trend Identification: Identifies and isolates trends by comparing against public complaint data.
Rapid Mitigation: Raises appropriate mitigation actions as soon as violations are detected.
High-Speed Processing: Uses LLMs and NLP to process massive amounts of data quickly, clearing backlogs efficiently.
Comprehensive Coverage: Scans all customer calls, providing much broader coverage than manual audits.

Use Cases of Shiboleth

Consumer Lending Compliance: Helps banks and lending institutions automate compliance checks for regulations like UDAAP and FDCPA.
Customer Interaction Audits: Automatically audits customer service calls and interactions for compliance issues.
Regulatory Reporting: Assists in creating first drafts of reports for government regulators based on analyzed data.
Backlog Clearance: Quickly processes large volumes of historical customer interaction data to clear compliance backlogs.

Pros

Significantly reduces manual audit work and costs
Provides more comprehensive compliance coverage than manual teams
Offers rapid detection and mitigation of compliance issues
Continuously updates based on latest regulatory trends and public data

Cons

May require integration with existing systems and processes
Potential for false positives in violation detection
Reliance on AI may raise concerns about explainability for some regulators